


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 						__a (							a_     ):
		_a      :    Optional[Any]           =      ['''image_processor''', '''tokenizer''']
		_a      :    Any           =      '''LayoutLMv3ImageProcessor'''
		_a      :    str           =      ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       **_SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     None
									if "feature_extractor" in kwargs:
																warnings.warn(
																    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																    ' instead.'     ,       _lowercase     ,       )
																_UpperCAmelCase											=     kwargs.pop('feature_extractor'		)

									_UpperCAmelCase											=     image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
																raise ValueError('You need to specify an `image_processor`.'		)
									if tokenizer is None:
																raise ValueError('You need to specify a `tokenizer`.'		)

									super().__init__(_lowercase     ,       _lowercase		)
		def __call__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = True     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = 0     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = True     ,       _SCREAMING_SNAKE_CASE = None     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			BatchEncoding:




									"""simple docstring"""





									if self.image_processor.apply_ocr and (boxes is not None):
																raise ValueError(
																    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'		)

									if self.image_processor.apply_ocr and (word_labels is not None):
																raise ValueError(
																    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'		)

									# first, apply the image processor
									_UpperCAmelCase											=     self.image_processor(images=_lowercase     ,       return_tensors=_lowercase		)

									# second, apply the tokenizer
									if text is not None and self.image_processor.apply_ocr and text_pair is None:
																if isinstance(_lowercase     ,       _lowercase		):
																							_UpperCAmelCase											=     [text]  # add batch dimension (as the image processor always adds a batch dimension)
																_UpperCAmelCase											=     features["""words"""]

									_UpperCAmelCase											=     self.tokenizer(
									    text=text if text is not None else features['words']     ,       text_pair=text_pair if text_pair is not None else None     ,       boxes=boxes if boxes is not None else features['boxes']     ,       word_labels=_lowercase     ,       add_special_tokens=_lowercase     ,       padding=_lowercase     ,       truncation=_lowercase     ,       max_length=_lowercase     ,       stride=_lowercase     ,       pad_to_multiple_of=_lowercase     ,       return_token_type_ids=_lowercase     ,       return_attention_mask=_lowercase     ,       return_overflowing_tokens=_lowercase     ,       return_special_tokens_mask=_lowercase     ,       return_offsets_mapping=_lowercase     ,       return_length=_lowercase     ,       verbose=_lowercase     ,       return_tensors=_lowercase     ,       **_lowercase     ,       )

									# add pixel values
									_UpperCAmelCase											=     features.pop('pixel_values'		)
									if return_overflowing_tokens is True:
																_UpperCAmelCase											=     self.get_overflowing_images(_lowercase     ,       encoded_inputs['overflow_to_sample_mapping']		)
									_UpperCAmelCase											=     images

									return encoded_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     []
									for sample_idx in overflow_to_sample_mapping:
																images_with_overflow.append(images[sample_idx]		)

									if len(_lowercase		) != len(_lowercase		):
																raise ValueError(
																    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
																    f''' {len(_lowercase		)} and {len(_lowercase		)}'''		)

									return images_with_overflow
		def       UpperCAmelCase__							(     self     ,       *_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)					->			Union[str, Any]:




									"""simple docstring"""





									return self.tokenizer.batch_decode(*_lowercase     ,       **_lowercase		)
		def       UpperCAmelCase__							(     self     ,       *_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)					->			str:




									"""simple docstring"""





									return self.tokenizer.decode(*_lowercase     ,       **_lowercase		)
		@property
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									return ["input_ids", "bbox", "attention_mask", "pixel_values"]
		@property
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,       _lowercase     ,       )
									return self.image_processor_class






		@property
		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,       _lowercase     ,       )
									return self.image_processor



import math


def 					_UpperCAmelCase				(						a__ = 1_0_0):

			'''simple docstring'''



			a_						:						List[str] 							= sum(i * i for i in range(1  ,	n + 1))
			a_						:						Optional[Any] 							= int(math.pow(sum(range(1  ,	n + 1))  ,	2))
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
	print(F"""{solution() = }""")


